Algorithmic Learning Theory
27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings
Seiten
2016
|
1st ed. 2016
Springer International Publishing (Verlag)
978-3-319-46378-0 (ISBN)
Springer International Publishing (Verlag)
978-3-319-46378-0 (ISBN)
This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.
Error bounds, sample compression schemes.- Statistical learning, theory, evolvability.- Exact and interactive learning.- Complexity of teaching models.- Inductive inference.- Online learning.- Bandits and reinforcement learning.- Clustering.
Erscheinungsdatum | 21.10.2016 |
---|---|
Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
Zusatzinfo | XIX, 371 p. 21 illus. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Schlagworte | Active learning • adversary models • Applications • Artificial Intelligence • artificial intelligence (incl. robotics) • boolean function learning • Clustering • Computer Science • conference proceedings • evolutionary algorithms • Inductive Inference • Informatics • Interactive Learning • Local Search • Models of learning • online learning algorithms • Online learning theory • Optimization • Perceptron • query learning • Reinforcement Learning • Research • Robotics • Sample complexity and generalization bounds • Semi-Supervised Learning • sequential decision making • structured prediction • Unsupervised Learning |
ISBN-10 | 3-319-46378-0 / 3319463780 |
ISBN-13 | 978-3-319-46378-0 / 9783319463780 |
Zustand | Neuware |
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